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There are thousands of academic papers on Arxiv, so which ones should you read? I read hundreds of GANs papers while researching for my book and below are the 12 most influential papers (from 2014 to 2019) I found. There aren’t that many breakthrough GANs papers after 2019. Click the names and images to go to source.
- Generative Adversarial Networks. The very first paper of GAN written by Ian GoodFellow et al in 2014. This paper describes GAN’s architecture that consists of generator and disciminator. It also provide mathematical derivation of adversarial loss.
- Auto-Encoding Variational Bayes. Variational autoencoder (VAE) showing encoding high dimensional pixels into small dimensional space. Many advanced GANs uses VAE as encoder.
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. DCGAN, established CNN architectures in the generator and the discriminator. Also demonstrate the use of vector arithmetic for latent space interpolation/exploration.
- Wasserstein GAN. This paper proves mathematically why GANs training is unstable. The Wasserstein loss is not widely used later but its approach of analysing GANs with mathematical rigour using Lipschitz constraint inspired innovations to make training GAN easier.
